Design space exploration using Computer-aided Engineering (CAE) analyses greatly benefits industrial product development. This is typically an iterative process involving a designer varying a set of parameters, and ultimately converging to an optimal set that achieves good expected functional characteristics as reported by the CAE analyses. However, the extent of CAE-based design exploration is currently limited due to the fact that products (such as aircrafts) are typically defined by a large number of design parameters, and computing CAE responses for a single set is a time consuming process thereby prohibiting the use of CAE for a large set of design variations.
Computational Fluid Dynamics (CFD) is an example of a tool employed in the iterative CAE process. CFD uses numerical methods and algorithms to solve and analyze problems that involve fluid flows. Performing CFD simulations is a time-consuming and performance-intensive activity. Additionally, CFD model preparation (including preparation of CFD mesh) is a very time-consuming and highly operator dependent task, often constituting the bottle neck of the process. This is often the result of tedious geometry cleanup and preparation, and challenges associated with obtaining a closed fluid volume. Repeating this process for each possible variation of a geometrical model becomes very computationally expensive. Thus, it would be valuable to have a reliable method to automatically identify the points in the parameter space that need to be analyzed in detail.
To date, the most direct and computationally expensive approach to evaluate design variations of a computer aided design (CAD) model is the generation of a detailed computational model and CFD simulation for several variations of all the considered parameters. This approach, if performed with a fine granularity of the design parameter-space sweep, heuristically allows the selection of a good approximation of the optimal design to achieve the desired technical performance. However, the realization of this approach becomes intractable in real applications. To address this issue, generally in practice, coarse granularity of parameter space is chosen. In addition, designers/engineers use their experience and comparison with past cases to manually reduce the features to explore. Often, in combination with this, approaches to reduce the computational cost of each CFD simulations are also implemented. This includes the use of high-performance computing, the use of reduced order modeling, and the combined use of reduced-order and full-order CFD simulation techniques.